Integrating Zero-Trust Architecture with Deep Learning Algorithm to Prevent Structured Query Language Injection Attack in Cloud Database

Authors

M. E Timadi Government House, Creek Haven, Yenagoa, Bayelsa
E.C.M Obasi Department of Computer Science and Informatics, Federal University, Otuoke, Bayelsa State

cyber attacks, SQL Injection Attack, zero-trust architecture, database protection, ML algorithm

The increasing reliance on cloud databases has made them a prime target for cyber attacks, with Structured

Query Language (SQL) injection being a particularly devastating threat. SQL injection attacks pose significant

threats to database security, compromising sensitive information. Deep learning algorithms have emerged as

effective solutions to detect and prevent SQL injection attacks. This study proposes a novel approach to

detecting SQL injection attack by integrating deep learning-based detection with zero-trust architectute. The

proposed system utilizes a Feed-Forward Neural Network (FNN)to analyze database queries and detect potential

SQL injection attacks. The FNN model is trained on a dataset of labelled queries, allowing it to learn patterns

and anomalies indictive of SQL injection attacks. The output of the FNN model is then integrated with zero-

trust architecture, which enforces strict access controls and authentication mechanisms based on the results

generated by the FNN model. The model exhibits a precision score approximating 100% accuracy in the

classification of queries deemed normal, while achieving a 94% rate of correct classification for queries

indicative of SQL injection attacks. By leveraging advanced machine learning techniques, our approach aims to

identify and block malicious queries in real-time, ensuring the integrity and security of cloud-based data.

Through a comprehensive evaluation, we demonstrate the effectiveness of our deep learning-based solution with

zero-trust architecture in detecting SQL injection attacks with high accuracy and low false positives.